Geometric camera calibration is a technique that estimates various parameters of a lens and image sensors of an image-capturing device, such as a camera. Usually, such parameters may refer to intrinsic and extrinsic camera parameters and distortion coefficients. Currently, the time required to calibrate intrinsic parameters of a camera may be proportional to the number of cameras in an imaging environment. This may involve manual capture of several images and computation of the intrinsic parameters by each camera in turns. Calibration of the extrinsic parameters may also depend on the intrinsic parameters. Thus, conventional camera calibration techniques for the intrinsic and/or extrinsic camera parameters may be a time-consuming process.
In certain scenarios, intrinsic and/or extrinsic camera parameter estimation techniques may employ a fixed two-dimensional (2-D) calibration object or texture pattern. In such scenarios, the position of each camera may be restricted by a viewing angle so that the texture pattern is discernible by the cameras. In certain other scenarios, camera extrinsic parameter estimation techniques may employ a three-dimensional (3-D) calibration object. A perspective distortion may make feature point detection and precise camera positioning more difficult. Thus, an advanced system and/or technique may be required for a quick and automated calibration of intrinsic and/or extrinsic camera parameters of one or more cameras, with increased accuracy.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.